红外热成像技术在FRP复合材料/热障涂层无损检测应用中的研究现状与进展

郑凯, 罗志涛, 张辉

郑凯, 罗志涛, 张辉. 红外热成像技术在FRP复合材料/热障涂层无损检测应用中的研究现状与进展[J]. 红外技术, 2023, 45(10): 1008-1019.
引用本文: 郑凯, 罗志涛, 张辉. 红外热成像技术在FRP复合材料/热障涂层无损检测应用中的研究现状与进展[J]. 红外技术, 2023, 45(10): 1008-1019.
ZHENG Kai, LUO Zhitao, ZHANG Hui. Research Status of Infrared Thermography in NDT of FRP Composites/Thermal Barrier Coatings and Its Development[J]. Infrared Technology , 2023, 45(10): 1008-1019.
Citation: ZHENG Kai, LUO Zhitao, ZHANG Hui. Research Status of Infrared Thermography in NDT of FRP Composites/Thermal Barrier Coatings and Its Development[J]. Infrared Technology , 2023, 45(10): 1008-1019.

红外热成像技术在FRP复合材料/热障涂层无损检测应用中的研究现状与进展

基金项目: 

国家重点研发计划 2022YFB3404300

国家自然科学基金 52272433

江苏省重点研发计划(产业前瞻与关键核心技术) BE2021084

江苏省市场监督管理局科技计划项目 KJ2022002

详细信息
    作者简介:

    郑凯(1967-),男,博士,研究员级高级工程师,研究方向:无损检测新技术应用。E-mail:kai.zheng@163.com

    通讯作者:

    张辉(1977-),男,教授,博士生导师,主要研究方向为复杂声场调控与声探测技术,多物理场无损检测新技术。E-mail:seuzhanghui@seu.edu.cn

  • 中图分类号: TB302.5

Research Status of Infrared Thermography in NDT of FRP Composites/Thermal Barrier Coatings and Its Development

  • 摘要: 红外热成像是具有非接触、检测面积大、检测结果直观等突出优势的新兴无损检测技术,近年来被广泛应用于金属、非金属、纤维增强复合材料(Fiber reinforced polymer,FRP)以及热障涂层等的无损检测与评价。本文首先简要介绍了红外热成像技术的基本原理和检测系统构成,特别是对光学、超声以及电磁等主要热激励形式的特点和优劣势进行了对比。然后,根据热激励形式的发展历程,详细介绍了光激励红外热成像技术在FRP复合材料和热障涂层无损检测与评价方面的研究现状与进展,重点关注了FRP复合材料/热障涂层热成像无损检测中的热难点问题。最后总结并展望了FRP复合材料/热障涂层红外热成像无损检测技术的未来发展趋势。
    Abstract: Infrared thermography is a new NDT technology with outstanding advantages such as non-contact, large detection area and intuitive detection results, and it has been widely used in NDT and evaluation of metal, non-metal, fiber reinforced polymer (FRP) and thermal barrier coatings. In this paper, the basic principle of infrared thermography technology and the composition of detection system are briefly introduced, especially the characteristics and advantages and disadvantages of optical, ultrasonic, and electromagnetic thermal excitation forms are compared. Then, according to the development history of thermal excitation forms, the research status and progress of optical excitation infrared thermography technology in the non-destructive testing and evaluation of FRP composites and thermal barrier coatings are introduced in detail, focusing on the hot and difficult problems in the non-destructive testing of FRP composites/thermal barrier coatings. Finally, the future development trend of infrared thermographic NDT technology for FRP composites/thermal barrier coatings is summarized and prospected.
  • 目标的三维重建被广泛地应用在生产生活、军事侦查等领域,其主要采用激光扫描点云重构的方法实现[1]。由于待测目标表面的反射率、粗糙度、入射角等的不同,从而对激光扫描结果产生很大的影响,故多手段结合、多方法集成、多数据格式融合的目标点云滤波与算法优化设计成为提高三维目标重建效果的研究热点之一[2]。激光扫描获得的点云数据是离散的,所以并不能像二维图像处理那样通过像素点邻域特征算法完成目标识别,从而造成目标局部点云数据提取时夹杂噪声点或剔除目标点,降低目标三维重建质量。故点云扫描常常需要配合相应的特征提取算法才能更好地完成目标三维重建,例如数模边界法、包络盒法、立体投影法、光谱限定法等[3-6]。数模边界法以待测目标数模作为点云边界条件,可以获得准确的局部特征,速度快、精度高,但不适用于非合作目标;王果等人[7]采用已知数模为车载激光雷达点云数据分离单木目标,可分度较优化前得到了大幅提升。包络盒法可以根据目标类型自适应调节点云选框范围,可适用于非合作目标、更灵活,但易受目标外形及照射角度影响。Dominik等人[8-9]采用三维包络盒反向投影目标点云数据,实现了车辆局部数据的快速提取,但算法在车型结构复杂时识别率仅达53.1%。立体投影法与以上两种方法相近,都是基于目标几何特性实现的,存在普适性差的问题。Mousavian[10]将目标投影方向的点云映射位置作为特征边界,获取自动驾驶中外部障碍物的目标三维位置,相比传统方法的测距精度提升了23.4%。

    相比之下,采用多光谱融合等手段提供边界限制的方法更具通用性。仝选悦[11]将红外特征作为点云提取边界,完成了非合作坦克目标的快速识别。Choi等[12]采用RGB分通道图像滤波的方式,对点云数据的特征点进行分离,噪声均值降低了约30%。王宏涛等[13]人采用融合光谱对机载LiDAR进行杂散点滤波,分类精度提升了13.4%。本文将多偏振光谱叠加形成目标局部特征区域,再把限定范围与激光扫描截面相结合,从而得到了经滤波抑噪的三维重建点云数据。

    图 1所示,本系统由3个部分组成:(a)偏振多光谱模块。处理模块控制步进电机选择不同的偏振片,入射光经偏振处理后照射在被测目标上,目标反射光由透镜组、检偏器接收光信号导入CCD;(b)双目激光扫描模块。扫描模块完成对目标整个区域的点云进行采集,CCD1和CCD2同时获取被测目标的激光回波信号,从而实现三维重建;(c)处理模块。将偏振多光谱模块的偏振图像数据作为映射边界条件,通过多光谱融合算法与点云特征对双目扫描的点云数据进行分类与降噪,最终完成目标三维视觉重建。

    图  1  多光谱融合的目标三维重建系统
    Figure  1.  Multi-spectrum fusion target 3D reconstruction system

    传统目标三维重建采用激光扫描实现,但扫描获得的点云数据往往受限于目标纹理、反光特性等影响而使局部重建误差过大,甚至出现数据空洞等现象。为了提高三维视觉重建效果,系统在扫描结构的基础上增加了(a)模块,通过步进电机驱动包含4个不同偏振态偏振片的旋转装置,使系统在任意扫描位置可以实现对目标表面分时采集4组偏振图像,并由CCD完成4幅偏振图像的采集。为了避免更换偏振片造成系统位置偏差导致的测试误差,选用旋转结构,即通过分时采集的方式保证空间位置不变,从而保证系统测试的稳定性。在测试过程中,采用偏振光源与双目成像系统中的光源同时照射待测目标,由于目标漫反射光与杂散光的偏振特性差异大,即在对原始多光谱进行适当滤波的条件下就能够提取目标光谱信息。同时,偏振光谱不受制于目标纹理特征,可以为三维点云数据提供准确的边界条件。

    线激光属于结构光,设其入射方向与基准面的入射角为θ1,漫反射光与基准面的法线夹角为θ2,根据三角测量关系可知:

    $$ \frac{X}{{X'}} \cdot \frac{{\sin ({\theta _1} + {\theta _2})}}{{\cos \alpha \cos {\theta _1}}} = \frac{{{L_1}\cos {\theta _1} - X\cos ({\theta _1} + {\theta _2})}}{{\left( {{L_2} - X'\cos \alpha } \right)\cos {\theta _1}}} $$ (1)

    式中:XX′分别为像面和标准面在X轴向的距离值;L1L2分别为CCD像面到透镜,透镜到标准面的距离;α为像面法向与光轴之间的夹角。当CCD放置于透镜焦距f位置时,则有:

    $$ X = \frac{{X'{L_1}\cos {\theta _1}\cos \alpha }}{{(f - X'\cos \alpha )\sin ({\theta _1} + {\theta _2}) + X'\cos ({\theta _1} + {\theta _2})\cos \alpha }} $$ (2)

    系统采用主点坐标标定方法,光路示意如图 2中双目成像部分。设主点的坐标是(u0, v0),任意目标点P1在像面的映射点为(u1, v1)。相机采集p1点的3个特征位置分别是(u1, v1)、(u2, v2)、(u3, v3),则可得:

    $$ \left({u}_{0}\text{,}{v}_{0}\right)=\left(\frac{2{u}_{2}{u}_{3}-{u}_{2}{u}_{1}-{u}_{1}{u}_{3}}{{u}_{3}-2{u}_{1}+{u}_{2}}\text{,}\frac{2{v}_{2}{v}_{3}-{v}_{2}{v}_{1}-{v}_{1}{v}_{3}}{{v}_{3}-2{v}_{1}+{v}_{2}}\right) $$ (3)
    图  2  多光谱偏振与双目系统的原理示意图
    Figure  2.  Multispectral polarization and binocular system

    通过标定完成主点位置提取,将Halcon标定板放置于与光轴平行的位置上,沿光轴方向平行运动,在预设位置上分别提取3张图像(P1P2P3所在平面,如图 2所示),把黑色圆心当作特征点位。

    根据菲涅尔原理,反射率会因光矢量方向的不同而不同,则通过旋转偏振片,可以改变反射光的亮度。在CCD前放置线偏振片,设偏振片透光轴和参考位置之间的夹角为v,则光照强度I(μ, λ, v)可表示为:

    $$ I\left( {\mu , \lambda , v} \right) = \frac{{{I_{\max }} + {I_{\min }}}}{2} + \frac{{{I_{\max }} - {I_{\min }}}}{2}\cos \left( {2v - 2\phi } \right) $$ (4)

    式中:μ为多光谱偏振图像中的像素点;λ为波长;ImaxImin分别为最大光强值和最小光强值;ϕ为偏振角。由于系统是正定的,故设系数矩阵x的解为x=[x1, x2, x3]T,则有:

    $$ \begin{array}{l}{I}_{\mathrm{max}}\text{=}{x}_{1}+\sqrt{{x}_{2}^{2}+{x}_{3}^{2}}\text{,}{I}_{\mathrm{min}}\text{=}{x}_{1}-\sqrt{{x}_{2}^{2}+{x}_{3}^{2}}\text{,}\\ \varphi \text{=0}\text{.5}\;\mathrm{arctan}\left({x}_{3}/{x}_{2}\right)\end{array} $$ (5)

    图 2所示,由于偏振光谱坐标系与双目测距坐标系在测试过程中是固定的,所以可以通过三维位置换算完成坐标系的统一。对于双目扫描系统而言,任意截面上的点云集合只能表征目标的距离信息,所以三维重建时容易产生局部位置分割错误的问题。采用多光谱偏振图像就相当于在对应截面上给出了一个具有偏振特性的边界区域(如图 2P1面上的阴影区),由此重建目标时有了明确的边界,则局部细节完整,避免了数据空洞与边界不清等问题。将(4)式代入(3)式,得到包含边界特征的坐标函数:

    $$ \left\{\begin{array}{l} \left(u_0, v_0\right)\;=\;\left(\frac{2 u_2 u_3-u_2 u_1-u_1 u_3}{u_3-2 u_1+u_2}, \frac{2 v_2 v_3-v_2 v_1-v_1 v_3}{v_3-2 v_1+v_2}\right) \\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;I_0 \geq I\left(\mu, \lambda, v)\right. \\ \left(u_0, v_0\right)=\left(u_{0+1}, v_{0+1}\right) \;\;\; I_0<I(\mu, \lambda, v) \end{array}\right.$$ (6)

    式中:I0表示根据目标表面特性预设的光强度识别阈值,只有当在有效测试范围内超过测试阈值的光强度值才会被保留。当I0I(μ, λ, v)时,则说明其符合阈值设定,标记为同一局部单元,即符合同一局部的边界特征。当I0I(μ, λ, v)时,该点序号加1,进入下一个局部分段的判断,以此类推,当迭代计算所有目标点时完成对目标的分类与识别。

    模型中需要获取3个位置的图像信息进行位置标定,激光器采用ColdRay-660 nm结构光激光器(实验中采用线型扫描),相机选用MER-240型面阵CCD,电控平台选用ZXT400MA06步进平台。CCD1和CCD2的间距为50.0 cm,标定板尺寸为60.0 cm×60.0 cm,标定板到相机像面距离为150.0 cm。因为系统完成后主要应用于工业生产流水线上目标表面质量的快速检测,其工作距离在1.2~1.8 m之间,故采用150.0 cm的标定距离。前置透镜组典型焦距1200 mm,可电控调焦,相机最高帧率为100 fps/s。系统与标定板如图 3所示。

    图  3  系统与标定板实物图
    Figure  3.  Photo of system and calibration board

    在获得线激光的能量分布后,对中心位置进行曲线拟合可以得到结构激光扫描线的拟合方程;然后通过两个CCD中标准板的图像位置,计算基线长度、测试目标与测试平台的夹角;最后将每个测量截面点云在三维空间复现,构成目标三维点云集合。以P1面的光强分布为例,对光束照射位置处能量进行采样拟合,如图 4(a),在对比了极值采样与高斯采样的基础上,选用了高斯采样,获得了亚像素级的标准位置坐标点,再结合Halcon标定板上圆点坐标,如图 4(b),即可将该截面的二维信息转化为三维点云。

    图  4  光束能量拟合与截面坐标
    Figure  4.  Beam energy fitting and section coordinates

    图 4(a)可以看出,高斯分布拟合的中心线对各个测试点的方差均值更小,当系统存在杂散点、照明不均匀、背景强光等时,其对总体分布的波动影响较小,而极值采样更偏向于峰值位置。在本系统应用中,高斯分布的拟合线更符合实际测试要求。在图 4(b)中,高斯采样拟合与极值采样拟合的直线映射位置与实际位置的偏差均值分别是0.59 mm与0.93 mm,故采用高斯采样误差更小。

    对于杂散光引入的白噪声干扰,仅采用传统灰度图像处理算法就能够很好的抑制,但在双目识别中采用的是激光扫描获取的方式,数据点是离散的,传统算法仅能计算某一界面,这样会导致目标不同局部区域存在混叠的问题。故单纯采用灰度图像处理算法无法对空间离散的白噪声进行有效抑制,所以限定滤波的边界显得尤为重要。本系统以CCD感光面法向为参考方向,设置前置偏振片线偏振度为0°、45°、90°和135°,采集4幅不同偏振图像I1I2I3I4,对4幅偏振光谱图像进行线性加权,生成单幅滤噪后图像,避免耀光、杂散光的干扰。将抑噪后图像中目标所在区域范围作为目标三维点云在该截面的二维边界。无偏振原始图像与经偏振多光谱叠加滤噪后的图像如图 5所示。

    图  5  线激光灰度图像经加权叠加滤波前后对比
    Figure  5.  Comparison of line laser gray image before and after weighted superposition filtering

    图 5(a)所示,线激光外的区域中存在的杂散光等白噪声并不是二维平面上的噪声,所以不能将其全部滤除,其中包含了目标其它部分的有效信号及不同分区的噪声,所以需要通过不同偏振角条件下点云数据的分区进行分别滤波。如图 5(b)所示,当根据4个偏振态图像的边界分区分别进行滤波后,从而得到解算后的融合图像,该图像完成了目标不同部分的分区滤波。滤波后线激光灰度图像的背景噪声被大幅降低,线激光照射的区域以外噪声强度均值得到了很好的抑制。取背景区域内10×10像素范围计算噪声强度均值,滤波前后分别为49.5和13.4,说明采用偏振多光谱叠加可以对不具备偏振特征的白噪声起到很好地抑制作用。而对5×5像素范围的线激光照射区域灰度图像进行分析发现,滤波前后分别为238.6和229.1,强度衰减远小于背景区,即滤波对提高图像信噪比是有效的。

    实验选用两段局部具有不同曲率的测试目标,对其进行扫描采集的点云数据,并利用偏振多光谱测试中提供的边际条件对点云数据进行滤波,获得目标三维可视化图像。在此基础上与数模位置对比,重建图像与偏差分布如图 6所示。

    图  6  基于多光谱融合的目标三维重建效果
    Figure  6.  3D reconstruction of target based on multi-spectral fusion

    图 6(a)中是在测试目标表面分别选取了100个坐标点,绝大部分分布在目标两个局部表面上。测试结果显示,大部分的测试点位重建精度偏差很小,超过80%的测试点的误差小于3.05 μm,这些点优化前后的平均偏差为1.49 μm。但未优化的测试点中存在3处明显偏差位置,偏差最大值为62.32 μm,显然已不在同一个表面,其被判定为杂散点噪声,可见采用本算法对点云内异常点具有很好的识别效果。由图 6(b)可以看出,经过优化滤波后的目标三维点云数据分布均匀,在主要的两个局部表面上基本没有杂散点,可以很好地反映目标表面结构,提供较好的视觉效果。

    本文针对三维目标重建过程中容易误将目标位置附近的杂散点混入的问题、对目标表面光滑度高容易产生数据空洞的问题,提出了基于偏振多光谱融合的双目激光扫描成像系统。双目激光扫描实验中,得到了高斯采样可以获得更好的中心位置反演精度的结论。偏振图像叠加测试时将目标界面的噪声进行了抑制,得到了较好的效果。最终,对待测目标点云数据的三维视觉重建得到了优化,验证了本算法的可行性。

  • 图  1   某航空发动机及其涡轮叶片热障涂层结构示意图

    Figure  1.   Schematic diagram of an aeroengine and its thermal barrier coating of turbine blade

    图  2   主动式红外热成像检测技术的主要分类依据及结果

    Figure  2.   Main classification basis and results of active infrared thermography detection technology

    图  3   光热无损检测原理及典型闪光灯激励热成像检测系统

    Figure  3.   Principle of photothermal nondestructive testing and typical flash excitation thermal imaging testing system

    图  4   截断相关光热相干层析成像检测技术原理:(a) 截断相关光热相干层析成像数学实施;(b) 激光诱导热成像系统框图

    Figure  4.   Principle of truncated correlation photothermal coherence tomography(TC-PCT) detection technology: (a) Mathematical implementation of truncated correlation photothermal coherence tomography; (b) Block diagram of laser induced thermal imaging system

    表  1   红外脉冲热成像、红外锁相热成像以及红外热波雷达成像检测技术的对比

    Table  1   Comparison of infrared pulse thermography, infrared lock-in thermography, and infrared thermal wave radar techniques

    Thermographic modality Excitation waveform form Advantage Shortcoming Application scope
    Infrared pulse thermography Fast detection speed, wide application range Shallow detection depth, large instantaneous energy, affected easily by the surface Impact damage and shallow defects
    Infrared lock-in thermography High signal-to-noise ratio, high sensitivity,
    low surface requirements
    Blind frequency phenomenon, multiple detection, low detection efficiency Deep defects
    Infrared thermal wave radar High signal-to-noise ratio, high resolution, large detection depth, low surface requirements, low energy, single reliable detection The detection time is slightly longer than pulse thermography, but much less than lock-in thermography Fast and reliable detection of deep defects
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  • [1]

    Clarke D R, Phillpot S R. Thermal barrier coatings materials[J]. Materials Today, 2005, 8(6): 22-29. DOI: 10.1016/S1369-7021(05)70934-2

    [2] 徐惠彬, 宫声凯, 刘福顺. 航空发动机热障涂层材料体系的研究[J]. 航空学报, 2000, 21(1): 7-12. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB200001002.htm

    XU Huibin, GONG Shengkai, LIU Fushun. Recent development in materials design of thermal barrier coating for gas turbine[J]. Acta Aeronautica et Astronautica Sinica, 2000, 21(1): 7-12. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB200001002.htm

    [3]

    Meier S M, Gupta D K. The evolution of thermal barrier coating in gas turbine engine applications[J]. Transaction of the ASME, 1994, 116(1): 205-257.

    [4]

    ZHAO Y X, LI D C, ZHONG X H, et al. Thermal shock behaviors of YSZ thick thermal barrier coatings fabricated by suspension and atmospheric plasma spraying[J]. Surface & Coatings Technology, 2014, 249: 48-55.

    [5] 郭洪波, 宫声凯, 徐惠彬. 先进航空发动机热障涂层技术研究进展[J]. 中国材料进展, 2009, 28(9-10): 18-25. https://www.cnki.com.cn/Article/CJFDTOTAL-XJKB2009Z2002.htm

    GUO Hongbo, GONG Shengkai, XU Huibin. Progress in thermal barrier coatings for advanced aeroengines[J]. Materials China, 2009, 28(9-10): 18-25. https://www.cnki.com.cn/Article/CJFDTOTAL-XJKB2009Z2002.htm

    [6]

    Steinberger R, Valadas Leitão T L, Ladstätter E, et al. Infrared thermographic techniques for non-destructive damage characterization of carbon fibre reinforced polymers during tensile fatigue testing[J]. International Journal of Fatigue, 2006, 28(10): 1340-1347. DOI: 10.1016/j.ijfatigue.2006.02.036

    [7]

    Suzuki Y, Todoroki A, Matsuzaki R, et al. Impact-damage visualization in CFRP by resistive heating: development of a new detection method for indentations caused by impact loads[J]. Composites Part A, 2012, 43(1): 53-64. DOI: 10.1016/j.compositesa.2011.09.003

    [8]

    Goidescu C, Welemane H, Garnier C, et al. Damage investigation in CFRP composites using full-field measurement techniques: combination of digital image stereo-correlation, infrared thermography and X-ray tomography[J]. Composites Part B, 2013, 48: 95-105. DOI: 10.1016/j.compositesb.2012.11.016

    [9]

    HE Y Z, TIAN G Y, PAN M C, et al. Impact evaluation in carbon fiber reinforced plastic(CFRP) laminates using eddy current pulsed thermography[J]. Composite Structures, 2014, 109(1): 1-7.

    [10]

    QU Z, JIANG P, ZHANG W X. Development and application of infrared thermography non-destructive testing techniques[J]. Sensors, 2020, 20: 3851. DOI: 10.3390/s20143851

    [11]

    Sophian A, TIAN G Y, Taylor D, et al. A feature extraction technique based on principal component analysis for pulsed eddy current NDT[J]. NDT & E International, 2003, 36(1): 37-41.

    [12] 郑凯, 江海军, 陈力. 红外热波无损检测技术的研究现状与进展[J]. 红外技术, 2018, 40(5): 401-411. http://hwjs.nvir.cn/article/id/hwjs201805001

    ZHENG Kai, JIANG Haijun, CHEN Li. Infrared thermography NDT and its development [J]. Infrared Technology, 2018, 40(5): 401-411. http://hwjs.nvir.cn/article/id/hwjs201805001

    [13]

    Almond D P, Lau S K. Edge effects and a method of defect sizing for transient thermography[J]. Applied Physics Letters, 1993, 62(25): 3369-3371. DOI: 10.1063/1.109074

    [14]

    Maldague X, Marinetti S. Pulse phase infrared thermography[J]. Journal of Applied Physics, 1996, 79(5): 2694-2698. DOI: 10.1063/1.362662

    [15]

    Ludwig N, Teruzzi P. Heat losses and 3D diffusion phenomena for defect sizing procedures in video pulse thermography[J]. Infrared Physics & Technology, 2002, 43(3-5): 297-301.

    [16]

    Maldague X, Ziadi A, Klein M. Double pulse infrared thermography[J]. NDT & E International, 2004, 37: 559-564.

    [17]

    Meola C, Carlomagno G M. Impact damage in GFRP: new insights with infrared thermography[J]. Composites Part A: Applied Science and Manufacturing, 2010, 41(12): 1839-1847. DOI: 10.1016/j.compositesa.2010.09.002

    [18]

    Almond D P, Pickering S G. An analytical study of the pulsed thermography defect detection limit[J]. Journal of Applied Physics, 2012, 111: 093510. DOI: 10.1063/1.4704684

    [19]

    Azizinasab B, Hasanzadeh R P R, Hedayatrasa S, et al. Defect detection and depth estimation in CFRP through phase of transient response of flash thermography[J]. IEEE Transactions on Industrial Informatics, 2022, 18(4): 2364-2373. DOI: 10.1109/TII.2021.3101492

    [20]

    Rajic N. Principal component thermography for flaw contrast enhancement and flaw depth characterization in composite structures[J]. Composite Structures, 2002, 58(4): 521-528. DOI: 10.1016/S0263-8223(02)00161-7

    [21]

    Marinetti S, Finesso L, Marsilio E. Matrix factorization methods: application to thermal NDT/E[J]. NDT & E International, 2006, 39(8): 611-616.

    [22]

    Alvarez-Restrepo C A, Benitez-Restrepo H D, Tobón L E. Characterization of defects of pulsed thermography inspections by orthogonal polynomial decomposition[J]. NDT & E International, 2017, 91: 9-21.

    [23]

    Yousefi B, Sfarra S, Sarasini F, et al. Low-rank sparse principal component thermography (sparse-PCT): comparative assessment on detection of subsurface defects[J]. Infrared Physics & Technology, 2019, 98: 278-284.

    [24] 姜千辉, 姜长胜, 葛庆平, 等. 红外热波序列图像的图像分割与三维显示[J]. 无损检测, 2008, 30(2): 100-103. https://www.cnki.com.cn/Article/CJFDTOTAL-WSJC200802011.htm

    JIANG Qianhui, JIANG Changsheng, GE Qingping, et al. Segmentation and 3D display of infrared thermal image[J]. Nondestructive Testing, 2008, 30(2): 100-103. https://www.cnki.com.cn/Article/CJFDTOTAL-WSJC200802011.htm

    [25]

    DUAN Y X, Servais P, Genest M, et al. ThermoPoD: a reliability study on active infrared thermography for the inspection of composite materials[J]. Journal of Mechanical Science and Technology, 2012, 26(7): 1985-1991. DOI: 10.1007/s12206-012-0510-8

    [26]

    WU J Y, Sfarra S, Yao Y. Sparse principal component thermography for subsurface defect detection in composite products[J]. IEEE Transactions on Industrial Informatics, 2018, 14(12): 5594-5600. DOI: 10.1109/TII.2018.2817520

    [27]

    WEN C M, Sfarra S, Gargiulo G, et al. Thermographic data analysis for defect detection by imposing spatial connectivity and sparsity constraints in principal component thermography[J]. IEEE Transactions on Industrial Informatics, 2018, 17(6): 3901-3909.

    [28]

    LIU L, GAO B, WU S C, et al. Structured iterative alternating sparse matrix decomposition for thermal imaging diagnostic system[J]. Infrared Physics & Technology, 2020, 107: 103288.

    [29]

    Ahmed J, GAO B, Woo W, et al. Ensemble joint sparse low rank matrix decomposition for thermography diagnosis system[J]. IEEE Transactions on Industrial Electronics, 2021, 68(3): 2648-2658. DOI: 10.1109/TIE.2020.2975484

    [30]

    ZHANG X F, HE Y Z, Chady T, et al. CFRP impact damage inspection based on manifold learning using ultrasonic induced thermography[J]. IEEE Transactions on Industrial Informatics, 2019, 15(5): 2648-2659. DOI: 10.1109/TII.2018.2866413

    [31]

    SHEN P, LUO Z T, WANG S, et al. Feature detection of GFRP subsurface defects using fast randomized sparse principal component thermography[J]. International Journal of Thermophysics, 2022, 43: 160. DOI: 10.1007/s10765-022-03076-z

    [32]

    Bates D, Smith G, LU D, et al. Rapid thermal non-destructive testing of aircraft components[J]. Composites Part B, 2000, 31(3): 175-185. DOI: 10.1016/S1359-8368(00)00005-6

    [33]

    Meola C, Carlomagno G M, Squillace A, et al. Non-destructive evaluation of aerospace materials with lock-in thermography[J]. Engineering Failure Analysis, 2006, 13(3): 380-388. DOI: 10.1016/j.engfailanal.2005.02.007

    [34]

    Pickering S, Almond D. Matched excitation energy comparison of the pulse and lock-in thermography NDE techniques[J]. NDT & E International, 2008, 41(7): 501-509.

    [35]

    Montanini R, Freni F. Non-destructive evaluation of thick glass fiber-reinforced composites by means of optically excited lock-in thermography[J]. Composites Part A, 2012, 43(11): 2075-2082. DOI: 10.1016/j.compositesa.2012.06.004

    [36]

    Lahiri B B, Bagavathiappan S, Reshmi P R, et al. Quantification of defects in composites and rubber materials using active thermography[J]. Infrared Physics & Technology, 2012(55): 191-199.

    [37]

    Oliveira B C F D, Nienheysen P, Baldo C R, et al. Improved impact damage characterization in CFRP samples using the fusion of optical lock-in thermography and optical square-pulse shearography images[J]. NDT & E international, 2020, 111: 102215.

    [38] 刘俊岩, 戴景民, 王扬. 红外图像序列处理的锁相热成像理论与试验[J]. 红外与激光工程, 2009, 38(2): 346-351. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ200902043.htm

    LIU Junyan, DAI Jingmin, WANG Yang. Theory and experiment of IR lock-in thermography with image sequence processing [J]. Infrared and Laser Engineering, 2009, 38(2): 346-351. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ200902043.htm

    [39] 刘俊岩, 戴景民, 王扬. 红外锁相法热波检测技术及缺陷深度测量[J]. 光学与精密工程, 2010, 18(1): 37-44. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJM201001007.htm

    LIU Junyan, DAI Jingmin, WANG Yang. Thermal wave detection and defect depth measurement based on lock-in thermography [J]. Optics and Precision Engineering, 2010, 18(1): 37-44. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJM201001007.htm

    [40]

    LIU J Y, WANG Y, DAI J M. Research on thermal wave processing of lock-in thermography based on analyzing image sequences for NDT[J]. Infrared Physics & Technology, 2010, 53(5): 348-357.

    [41]

    GONG J L, LIU J Y, WANG F, et al. Inverse heat transfer approach for nondestructive estimation the size and depth of subsurface defects of CFRP composite using lock-in thermography[J]. Infrared Physics & Technology, 2015, 71: 439-447.

    [42]

    LIU J Y, WANG F, LIU Y, et al. Inverse methodology for identification the thermal diffusivity and subsurface defect of CFRP composite by lock-in thermographic phase (LITP) profile reconstruction[J]. Composite Structures, 2016, 138: 214-226. DOI: 10.1016/j.compstruct.2015.11.062

    [43]

    CAO Y P, DONG Y F, CAO Y L, et al. Two-stream convolutional neural network for non-destructive subsurface defect detection via similarity comparison of lock-in thermography signals[J]. NDT & E International, 2020, 112: 102246.

    [44]

    DONG Y F, XIA C J, YANG J X, et al. Spatio-temporal 3-D residual networks for simultaneous detection and depth estimation of CFRP subsurface defects in lock-in thermography[J]. IEEE Transactions on Industrial Informatics, 2022, 18(4): 2571-2581. DOI: 10.1109/TII.2021.3103019

    [45]

    DONG Y F, ZHAO B W, YANG J X, et al. Two-stage convolutional neural network for joint removal of sensor noise and background interference in lock-in thermography[J]. NDT & E International, 2023, 137: 102816.

    [46]

    LUO Z T, LUO H, WANG S, et al. Enhanced CFRP defect detection from highly undersampled thermographic data via low-rank tensor completion-based thermography[J]. IEEE Transactions on Industrial Informatics, 2022, 18(12): 8641-8653. DOI: 10.1109/TII.2022.3154786

    [47]

    Tabatabaei N, Mandelis A. Thermal-wave radar: a novel subsurface imaging modality with extended depth-resolution dynamic range[J]. Review of Scientific Instruments, 2009, 80(3): 034902. DOI: 10.1063/1.3095560

    [48]

    Tabatabaei N, Mandelis A, Amaechi B T. Thermophotonic radar imaging: An emissivity-normalized modality with advantages over phase lock-in thermography[J]. Applied Physics Letters, 2011, 98(16): 163706. DOI: 10.1063/1.3582243

    [49]

    Mulaveesala R, Tuli S. Theory of frequency modulated thermal wave imaging for nondestructive subsurface defect detection[J]. Applied Physics Letters, 2006, 89: 191913. DOI: 10.1063/1.2382738

    [50]

    Mulaveesala R, Vaddi J S, Singh P. Pulse compression approach to infrared nondestructive characterization[J]. Review of Scientific Instruments, 2008, 79(9): 094901. DOI: 10.1063/1.2976673

    [51]

    Tabatabaei N, Mandelis A. Thermal coherence tomography using match filter binary phase coded diffusion waves[J]. Physics Review Letters, 2011, 107: 165901. DOI: 10.1103/PhysRevLett.107.165901

    [52]

    Kaiplavil S, Mandelis A. Truncated-correlation photothermal coherence tomography for deep subsurface analysis[J]. Nature Photonics, 2014, 8(8): 635-642. DOI: 10.1038/nphoton.2014.111

    [53]

    Chatterjee K, Tuli S, Pickering S G, et al. A comparison of the pulsed, lock-in and frequency modulated thermography nondestructive evaluation techniques[J]. NDT & E International, 2011, 44(7): 655-667.

    [54]

    Giorleo G, Meola C. Comparison between pulsed and modulated thermography in glass–epoxy laminates[J]. NDT & E International, 2002, 35(5): 287-292.

    [55]

    Dua G, Arora V, Mulaveesala R. Defect detection capabilities of pulse compression based infrared non-destructive testing and evaluation[J]. IEEE Sensors Journal, 2020, 21(6): 7940-7947.

    [56]

    Rani A, Mulaveesala R. Novel pulse compression favorable excitation schemes for infrared non-destructive testing and evaluation of glass fibre reinforced polymer materials[J]. Composite Structures, 2022, 286: 115338. DOI: 10.1016/j.compstruct.2022.115338

    [57]

    Hedayatrasa S, Poelman G, Segers J, et al. Performance of frequency and/or phase modulated excitation waveforms for optical infrared thermography of CFRPs through thermal wave radar: a simulation study[J]. Composite Structures, 2019, 225: 111177. DOI: 10.1016/j.compstruct.2019.111177

    [58]

    Hedayatrasa S, Poelman G, Segers J, et al. Novel discrete frequency-phase modulated excitation waveform for enhanced depth resolvability of thermal wave radar[J]. Mechanical Systems and Signal Processing, 2019, 132: 512-522. DOI: 10.1016/j.ymssp.2019.07.011

    [59]

    Hedayatrasa S, Poelman G, Segers J, et al. On the application of an optimized frequency-phase modulated waveform for enhanced infrared thermal wave radar imaging of composites[J]. Optics and Lasers in Engineering, 2021, 138: 106411. DOI: 10.1016/j.optlaseng.2020.106411

    [60]

    GONG J L, LIU J Y, QIN L, et al. Investigation of carbon fiber reinforced polymer (CFRP) sheet with subsurface defects inspection using thermal-wave radar imaging (TWRI) based on the multi-transform technique[J]. NDT & E International, 2014, 62: 130-136.

    [61]

    WANG F, LIU J Y, LIU Y, et al. Research on the fiber lay-up orientation detection of unidirectional CFRP laminates composite using thermal-wave radar imaging[J]. NDT & E International, 2016, 84: 54-66.

    [62]

    WANG F, WANG Y H, LIU J Y, et al. Optical excitation fractional Fourier transform (FrFT) based enhanced thermal-wave radar imaging (TWRI)[J]. Optics Express, 2018, 26(17): 21403. DOI: 10.1364/OE.26.021403

    [63]

    YANG R Z, HE Y Z, Mandelis A, et al. Induction infrared thermography and thermal-wave-radar analysis for imaging inspection and diagnosis of blade composites[J]. IEEE Transactions on Industrial Informatics, 2018, 14(12): 5637-5647. DOI: 10.1109/TII.2018.2834462

    [64]

    WU S C, GAO B, YANG Y, et al. Halogen optical referred pulse-compression thermography for defect detection of CFRP[J]. Infrared Physics & Technology, 2019, 102: 103006.

    [65]

    LUO Z T, SHEN P, LUO H, et al. Advanced orthogonal frequency and phase modulated waveform for contrast-enhanced photothermal wave radar thermography[J]. Journal of Applied Physics, 2022, 131(22): 224903. DOI: 10.1063/5.0087734

    [66]

    GUO W, DONG L H, WANG H D, et al. Discriminate the substrate crack under sprayed coatings using ultrasonic infrared thermography[J]. Infrared Physics & Technology, 2019, 102(9): 103073.

    [67]

    GUO W, HUANG J K, ZHU J G, et al. Experimental investigation on detection of coating debonds in thermal barrier coatings using vibrothermography with a piezoceramic actuator[J]. NDT & E International, 2023, 137(7): 102859.

    [68]

    ZHU W Y, LIU Z W, JIAO D C, et al. Eddy current thermography with adaptive carrier algorithm for non-destructive testing of debonding defects in thermal barrier coatings[J]. Journal of Nondestructive Evaluation, 2018, 37: 31. DOI: 10.1007/s10921-018-0483-3

    [69]

    Cielo P, Dallaire S. Optothermal NDE of thermal-barrier coatings[J]. Journal of Materials Engineering, 1987, 9: 71-79. DOI: 10.1007/BF02833789

    [70]

    LIU H N, Sakamoto M, Kishi K, et al. Detection of defects in thermal barrier coatings by thermography analysis[J]. Materials Transactions, 2003, 44(9): 1845-1850. DOI: 10.2320/matertrans.44.1845

    [71]

    Shepard S M, Ahmed T, Rubadeux B A, et al. Synthetic processing of pulsed thermographic data for inspection of turbine components[J]. Insight-non-Destructive Testing and Condition Monitoring, 2001, 43(9): 587-589.

    [72]

    Shepard S M, Lhota J R, Rubadeux B A, et al. Reconstruction and enhancement of active thermographic image sequences[J]. Optical Engineering, 2003, 42(5): 1337-1342. DOI: 10.1117/1.1566969

    [73]

    Shepard S M, HOU Y L, Lhota J R, et al. Thermographic measurement of thermal barrier coating thickness[C]//Proceedings of SPIE, 2005, 5782: 407-410.

    [74]

    Marinetti S, Vavilov V, Bison P, et al. Quantitative infrared thermographic nondestructive testing of thermal barrier coatings[J]. Materials Evaluation, 2003, 61(6): 773-780.

    [75]

    Marinetti S, Robba D, Cernuschi F, et al. Thermographic inspection of TBC coated gas turbine blades: discrimination between coating over-thicknesses and adhesion defects[J]. Infrared Physics & Technology, 2007, 49(3): 281-285.

    [76]

    Cernuschi F, Marinetti S. Discrimination between over-thickness and delamination of thermal barrier coatings by apparent thermal effusivity thermographic technique[J]. Journal of Thermal Spray Technology, 2010, 19(5): 958-963. DOI: 10.1007/s11666-010-9493-0

    [77]

    Bison P, Cernuschi F, Grinzato E. In-depth and in-plane thermal diffusivity measurements of thermal barrier coatings by IR camera: evaluation of ageing[J]. International Journal of Thermophysics, 2008, 29(6): 2149-2161. DOI: 10.1007/s10765-008-0421-1

    [78]

    Cernuschi F, Bison P, Figari A, et al. Thermal diffusivity measurements by photothermal and thermographic techniques[J]. International Journal of Thermophysics, 2004, 25(2): 439-457. DOI: 10.1023/B:IJOT.0000028480.27206.cb

    [79]

    Cernuschi F, Bison P, Marinetti S, et al. Thermal diffusivity measurement by thermographic technique for the non-destructive integrity assessment of TBCs coupons[J]. Surface and Coatings Technology, 2010, 205(2): 498-505. DOI: 10.1016/j.surfcoat.2010.07.024

    [80]

    Bison P, Cernuschi F, Capelli S. A thermographic technique for the simultaneous estimation of in-plane and in-depth thermal diffusivities of TBCs[J]. Surface and Coatings Technology, 2011, 205(10): 3128-3133. DOI: 10.1016/j.surfcoat.2010.11.013

    [81]

    Cernuschi F. Can TBC porosity be estimated by non-destructive infrared techniques? a theoretical and experimental analysis[J]. Surface and Coatings Technology, 2015, 272: 387-394. DOI: 10.1016/j.surfcoat.2015.03.036

    [82]

    Franke B, Sohn Y H, CHEN X, et al. Monitoring damage evolution in thermal barrier coatings with thermal wave imaging[J]. Surface and Coatings Technology, 2005, 200(5-6): 1292-1297. DOI: 10.1016/j.surfcoat.2005.07.090

    [83]

    Choi C, Choi S H, Kim J. Study for blade ceramic coating delamination detection for gas turbine[J]. International Journal of Modern Physics B, 2008, 22(31n32): 5699-5704. DOI: 10.1142/S0217979208051030

    [84]

    Schweda M, Beck T, Offermann M, et al. Thermographic analysis and modelling of the delamination crack growth in a thermal barrier coating on Fecralloy substrate[J]. Surface and Coatings Technology, 2013, 217: 124-128. DOI: 10.1016/j.surfcoat.2012.12.002

    [85]

    Schweda M, Beck T, Malzbender J, et al. Damage evolution of a thermal barrier coating system with 3-dimensional periodic interface roughness: effects of roughness depth, substrate creep strength and pre-oxidation[J]. Surface and Coatings Technology, 2015, 276: 368-373. DOI: 10.1016/j.surfcoat.2015.06.046

    [86]

    Tinsley L, Chalk C, Nicholls J, et al. A study of pulsed thermography for life assessment of thin EB-PVD TBCs undergoing oxidation ageing[J]. NDT & E International, 2017, 92: 67-74.

    [87]

    Ptaszek G, Cawley P, Almond D, et al. Artificial disbonds for calibration of transient thermography inspection of thermal barrier coating systems[J]. NDT & E International, 2012, 45(1): 71-78.

    [88]

    Ptaszek G, Cawley P, Almond D, et al. Transient thermography testing of unpainted thermal barrier coating (TBC) systems[J]. NDT & E International, 2013, 59: 48-56.

    [89]

    Ptaszek G. Investigation and development of transient thermography for detection of disbonds in thermal barrier coating systems[D]. Imperial College London, 2012.

    [90]

    Mezghani S, Perrin E, Vrabie V, et al. Evaluation of paint coating thickness variations based on pulsed infrared thermography laser technique[J]. Infrared Physics & Technology, 2016, 76: 393-401.

    [91]

    Unnikrishnakurup S, Dash J, Ray S, et al. Nondestructive evaluation of thermal barrier coating thickness degradation using pulsed IR thermography and THz-TDS measurements: a comparative study[J]. NDT & E International, 2020, 116: 102367.

    [92] 郭兴旺, 丁蒙蒙. 热障涂层红外热无损检测的建模和有限元分析[J]. 北京航空航天大学学报, 2009, 35(2): 174-178. DOI: 10.13700/j.bh.1001-5965.2009.02.005

    GUO Xingwang, DING Mengmeng. Modeling and finite element analysis of thermal barrier coatings in IR NDT[J]. Journal of Beijing University of Aeronautics and Astronautics, 2009, 35(2): 174-178. DOI: 10.13700/j.bh.1001-5965.2009.02.005

    [93] 郭兴旺, 丁蒙蒙. 热障涂层厚度及厚度不均热无损检测的数值模拟[J]. 航空学报, 2010, 31(1): 198-203. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201001031.htm

    GUO Xingwang, DING Mengmeng. Simulation of thermal NDT of thickness and its unevenness of thermal barrier coatings [J]. Acta Aeronautica et Astronautica Sinica, 2010, 31(1): 198-203. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201001031.htm

    [94]

    ZHAO S B, ZHANG C L, WU N M, et al. Quality evaluation for air plasma spray thermal barrier coatings with pulsed thermography[J]. Progress in Natural Science: Materials International, 2011, 21(4): 301-306. DOI: 10.1016/S1002-0071(12)60061-6

    [95]

    ZHAO S B, WANG H M, WU N M, et al. Nondestructive testing of the fatigue properties of air plasma sprayed thermal barrier coatings by pulsed thermography[J]. Russian Journal of Nondestructive Testing, 2015, 51: 445-456. DOI: 10.1134/S1061830915070074

    [96] 刘颖韬, 牟仁德, 郭广平, 等. 热障涂层闪光灯激励红外热像检测[J]. 航空材料学报, 2015, 35(6): 83-90. https://www.cnki.com.cn/Article/CJFDTOTAL-HKCB201506014.htm

    LIU Yingtao, MAO Rende, GUO Guangping, et al. Infrared flash thermographic nondestructive testing of defects in thermal barrier coating [J]. Journal of Aeronautical Materials, 2015, 35(6): 83-90. https://www.cnki.com.cn/Article/CJFDTOTAL-HKCB201506014.htm

    [97] 陈林, 杨立, 范春利, 等. 基于相位的热障涂层厚度及其脱粘缺陷红外定量识别[J]. 红外与激光工程, 2015, 44(7): 2050-2056. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ201507015.htm

    CHEN Lin, YANG Li, FAN Chunli, et al. Quantitative identification of coating thickness and debonding defects of TBC by pulse phase technology [J]. Infrared and Laser Engineering, 2015, 44(7): 2050-2056. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ201507015.htm

    [98]

    BU C W, TANG Q J, LIU Y L, et al. Quantitative detection of thermal barrier coating thickness based on simulated annealing algorithm using pulsed infrared thermography technology[J]. Applied Thermal Engineering, 2016, 99: 751-755. DOI: 10.1016/j.applthermaleng.2016.01.143

    [99]

    TANG Q J, LIU J Y, DAI J M, et al. Theoretical and experimental study on thermal barrier coating (TBC) uneven thickness detection using pulsed infrared thermography technology[J]. Applied Thermal Engineering, 2017, 114: 770-775. DOI: 10.1016/j.applthermaleng.2016.12.032

    [100]

    TANG Q J, DAI J M, LIU J Y, et al. Quantitative detection of defects based on Markov-PCA-BP algorithm using pulsed infrared thermography technology[J]. Infrared Physics & Technology, 2016, 77: 144-148.

    [101]

    BU C W, SUN Z H, TANG Q J, et al. Thermography sequence processing and defect edge identification of TBC structure debonding defects detection using long-pulsed infrared wave non-destructive testing technology[J]. Russian Journal of Nondestructive Testing, 2019, 55: 80-87. DOI: 10.1134/S1061830919010030

    [102] 郭伟, 董丽虹, 王海斗, 等. 基于小波分解的热波相位特征提取及喷涂层厚度评价[J]. 红外与激光工程, 2017, 46(9): 81-87. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ201709014.htm

    GUO Wei, DONG Lihong, WANG Haidou, et al. Phase spectra extract of thermal wave with wavelet decomposition and coating thickness estimation[J]. Infrared and Laser Engineering, 2017, 46(9): 81-87. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ201709014.htm

    [103] 董丽虹, 郭伟, 王海斗, 等. 热障涂层界面脱粘缺陷的脉冲红外热成像检测[J]. 航空学报, 2019, 40(8): 422895. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201908024.htm

    DONG Lihong, GUO Wei, WANG Haidou, et al. Phase spectra extract of thermal wave with wavelet decomposition and coating thickness estimation [J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(8): 422895. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201908024.htm

    [104]

    GUO W, DONG L H, WANG H D, et al. Size estimation of coating disbonds using the first derivative images in pulsed thermography[J]. Infrared Physics & Technology, 2020, 104: 103106.

    [105]

    LIU Z W, JIAO D C, SHI W X, et al. Linear laser fast scanning thermography NDT for artificial debond defects in thermal barrier coatings[J]. Optics Express, 2017, 25(25): 31789. DOI: 10.1364/OE.25.031789

    [106]

    JIAO D C, LIU Z W, ZHU W Y, et al. Exact localization of debonding defects in thermal barrier coatings[J]. AIAA Journal, 2018, 56(9): 3691-3700. DOI: 10.2514/1.J056806

    [107]

    CHEN F, ZHANG K, JIANG H J, et al. Thickness evaluations for thin coatings using laser scanning thermography[J]. NDT & E International, 2023, 137(17): 102817.

    [108]

    JIAO D C, SHI W X, LIU Z W, et al. Laser multi-mode scanning thermography method for fast inspection of micro-cracks in TBCs surface[J]. Journal of Nondestructive Evaluation, 2018, 37: 30. DOI: 10.1007/s10921-018-0485-1

    [109]

    Shrestha R, Kim W. Evaluation of coating thickness by thermal wave imaging: a comparative study of pulsed and lock-in infrared thermography - Part Ⅰ: simulation[J]. Infrared Physics & Technology, 2017, 83: 124-131.

    [110]

    Shrestha R, Kim W. Evaluation of coating thickness by thermal wave imaging: a comparative study of pulsed and lock-in infrared thermography – Part Ⅱ: experimental investigation[J]. Infrared Physics & Technology, 2018, 92: 24-29.

    [111]

    ZHANG J Y, MENG X B, MA Y C. A new measurement method of coatings thickness based on lock-in thermography[J]. Infrared Physics & Technology, 2016, 76: 655-660.

    [112]

    TANG Q J, DAI J M, BU C W, et al. Experimental study on debonding defects detection in thermal barrier coating structure using infrared lock-in thermographic technique[J]. Applied Thermal Engineering, 2016, 107: 463-468.

    [113]

    SONG P, XIAO P, LIU J Y, et al. The inspection of coating thickness uniformity of SiC-coated carbon-carbon (C/C) composites by laser-induced thermal-wave imaging [J]. Carbon, 2019, 147: 348-356.

    [114]

    SHI L C, LONG Y, WANG Y Z, et al. Online nondestructive evaluation of TBC crack using infrared thermography[J]. Measurement Science and Technology, 2021, 32(11): 115008.

    [115]

    WANG F, LIU J Y, Mohummad O, et al. Research on debonding defects in thermal barrier coatings structure by thermal-wave radar imaging (TWRI)[J]. International Journal of Thermophysics, 2018, 39: 71.

    [116]

    LUO Z T, LUO H, WANG S, et al. The photothermal wave field and high-resolution photothermal pulse compression thermography for ceramic/metal composite solids[J]. Composite Structures, 2022, 282(4): 115069.

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  • 收稿日期:  2023-08-30
  • 修回日期:  2023-10-07
  • 刊出日期:  2023-10-19

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